Molecular Profiling to Predict Response to Chemotherapy

描述（由申请人提供）：目前，晚期上皮性卵巢癌患者的标准治疗包括原发性手术细胞减少，随后是原发性铂/紫杉烷化疗。虽然大多数患者最初有完全的临床反应，但少数患者在治疗后仍无反应或病情进展，大多数患者在初次治疗后会复发。患有这种“铂耐药”疾病的患者采用补救性化疗治疗，预后较差。本提案中所述工作的目标是确定预测晚期卵巢癌化疗反应的基因表达模式。我们还旨在进一步表征导致化学敏感性的基因的作用。该提案的R21回顾性阶段旨在开发基因表达谱，预测对原发性和补救性化疗的反应。这将通过从H. Lee Moffitt癌症中心和杜克大学医学中心的肿瘤库中获得的卵巢癌回顾性微阵列分析来完成。将开发计算工具来定义预测治疗反应的基因谱。基因表达特征将在R33赞助的前瞻性临床试验中得到验证和完善。前瞻性收集的卵巢样本将被排列，并观察对原发性和补救性治疗的临床反应。此外，我们的目标是探索机会，通过对在挽救性治疗开始之前获得的复发性卵巢癌活检（或腹水）样本进行微阵列表达分析，来提高我们预测挽救性治疗反应的能力。为了扩展我们的阵列发现，涉及预测模型的基因和基因通路将通过先进的生物信息学工具和定量PCR在大量卵巢癌中进行额外的分析。预测卵巢癌化疗反应的能力将使根据癌症表达谱为个体患者制定量身定制的治疗方案。这样，可以提高反应率，避免毒性药物，保留骨髓，提高生活质量。最终，确定治疗反应的生物学基础将有助于开发更多可能提高卵巢癌治愈率的活性药物。

DESCRIPTION (provided by applicant): Currently, standard care for patients with advanced stage epithelial ovarian cancer includes primary surgical cytoreduction followed by primary platinum/taxane chemotherapy. Although the majority of patients initially experience a complete clinical response, a minority will have unresponsive or progressive disease despite therapy, and most will experience a recurrence following primary treatment. Patients with such "platinum resistant" disease are treated with salvage chemotherapy and have a poor prognosis. The goal of the work described in this proposal is to identify patterns of gene expression that predict response to chemotherapy for advanced stage ovarian cancers. We also aim to further characterize the role of the genes that contribute to chemosensitivity. The R21 retrospective phase of this proposal aims to develop gene expression profiles that predict response to primary and salvage chemotherapy. This will be accomplished by a retrospective microarray analysis of ovarian cancers obtained from the tumor banks of the H. Lee Moffitt Cancer Center and Duke University Medical Center. Computational tools will be developed to define gene profiles that predict response to therapy. The gene expression signatures will be validated and refined in the R33 sponsored prospective clinical trial. Prospectively collected ovarian samples will be arrayed and the clinical response to primary and salvage therapy observed. Additionally, we aim to explore opportunities to improve our ability to predict response to salvage therapy by performing microarray expression analysis of recurrent ovarian cancer biopsy (or ascites) samples obtained prior to the initiation of salvage therapies. To extend our array findings, genes and gene pathways involved in the predictive model will be subject to additional analysis by advanced bioinformatics tools and quantitative PCR in a larger number of ovarian cancers. The ability to predict response to chemotherapy for ovarian cancer will enable tailored therapeutic regimens to be established for individual patients on the basis of cancer expression profiles. As such, response rates can be improved, toxic agents avoided, bone marrow spared, and quality of life enhanced. Ultimately, defining the biologic underpinnings of response to therapy will facilitate the development of more active agents that may improve cure rates for ovarian cancer.